IMGA: Efficient In-Memory Graph Convolution Network Aggregation With Data Flow Optimizations-Reference-Cited by-同舟云学术

IMGA: Efficient In-Memory Graph Convolution Network Aggregation With Data Flow Optimizations

Published:2023-12 Issue:12 Volume:42 Page:4695-4705
ISSN:0278-0070
Container-title:IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
language:
Short-container-title:IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Author:

Wei Yuntao¹,Wang Xueyan¹^ORCID,Zhang Shangtong¹,Yang Jianlei²^ORCID,Jia Xiaotao¹^ORCID,Wang Zhaohao¹^ORCID,Qu Gang³^ORCID,Zhao Weisheng¹

Affiliation:

1. MIIT Key Laboratory of Spintronics, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, China

2. School of Computer Science and Engineering, BDBC, State Key Laboratory of Software Development Environment, Beihang University, Beijing, China

3. Department of Electrical and Computer Engineering, Institute for Systems Research, University of Maryland at College Park, College Park, MD, USA

Funder

National Natural Science Foundation of China

Joint Funds of the National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Software

Link

Reference36 articles.

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